Background: Valid causal inference from observational pharmacoepidemiologic studies relies on adequately adjusting for confounding. Aims: The goal of this article is to provide clarity and guidance on issues related to confounding and provide motivation for using more flexible models for causal inference in pharmacoepidemiology. Materials & Methods: In this article we elucidate two important components of making valid inference from observational data: measuring the necessary set of variables at the design/data collection phase (measured confounding) and properly accounting for confounding at the modeling/analysis phase (accounted-for confounding). For the latter concept, we contrast parametric modeling approaches, which are susceptible to model misspecification bias, with data adaptive approaches. Discussion: Both measuring and properly accounting for confounding is critical to obtaining valid causal inference from pharmacoepidemiology studies. Carefully thought out DAGs, based on subject matter knowledge, can help to better identify confounders and confounding. Even when confounding has been adequately measured, mis-specified models may lead to unaccounted for confounding and increasing the sample size often does not help. We recommend modern analytic techniques such as flexible data adaptive approaches that do not rely on strong parametric assumptions. Further, sensitivity analyses and other modern bounding approaches are recommended to account for the effects of unmeasured confounding. Conclusion: Confounding must be considered at both the design and analysis stages of a study. DAGs and data adaptive approaches can help.
All Science Journal Classification (ASJC) codes
- Pharmacology (medical)
- causal inference
- observational studies